Highlights
- The dataset: We analyzed over 260,000 AI source citations generated from prompts specific to the hotel industry. The AI responses behind them come from four AI models and were run in English and six additional language markets over the course of Q2 2026.
- Brand-direct hotel sites climbed in the window: Fairmont, Four Seasons, Hilton, and Marriott all rose in relative rank as travel-media aggregators fell.
- Only 24% of citations in non-English prompts are local: Global and English-language sites dominate hotel answers even when travelers search in their own language.
- ~25% average overlap between any two models: Any two of the four share only about a quarter of their top-20 cited hotel domains.
- 56% of Microsoft Copilot's citations are from commercial domains: Booking sites and hotel brands, making Microsoft Copilot the most commercial model by far.
- 51% of Microsoft Copilot's Swedish citations are local: Microsoft Copilot cites local .se domains most, while Grok stays global at 33%.
Which sources should you target to get cited as a hotel brand?
Each model relies on its own distinctive top domains.
| Model | Top sources | Citation character |
|---|---|---|
| ChatGPT | Reddit, oyster.com, timeout.com, vogue.com, en.wikipedia.org | UGC + editorial / magazine |
| Microsoft Copilot | marriott.com, fourseasons.com, fairmont.com, hilton.com, ritzcarlton.com | Brand-direct + booking |
| Grok | tripadvisor.com, travel.usnews.com, cntraveler.com, forbestravelguide.com, designhotels.com | Travel media at scale |
| Google AI Overview | google.com, instagram.com, tiktok.com, youtube.com, tripadvisor.com | Own properties + social |
TripAdvisor is the closest thing to common ground, it appears in every model's top tier, ranking #1 for both Grok and Microsoft Copilot, but its rank varies down to #3 for ChatGPT, which puts Reddit first.
How have AI source rankings changed over time in the hotel industry?
We split the ~16-week observation window at its midpoint and re-ranked domains by citation volume in each half. Relative rank is the reliable signal here, not raw volume.
The clearest narrative is a rotation from third-party discovery sources toward first-party brand domains. Four Seasons, Fairmont, Hilton, and Marriott all gained relative ground, while curated travel-media lists (Forbes Travel Guide, Design Hotels) and OTAs (Hotels.com, Expedia) lost it. AI citation rankings are dynamic, not static, track them in the live hotel AI visibility rankings.
What type of content do AI models cite for hotels?
The models differ not just on which domains but on what type of content they trust. We classified every cited domain into six categories.
Microsoft Copilot is the commercial engine: 55.9% of its citations go to booking platforms and hotel-brand sites. ChatGPT is the most editorial, sending 41.7% of its citations to travel media and magazines, and is the only model that meaningfully cites reference sources like Wikipedia (5.6% versus under 1.6% everywhere else). Google AI Overview tilts commercial too (43.4%), while Grok spreads across editorial and UGC. A strong editorial PR footprint pays off most on ChatGPT and Grok; owned booking and brand pages carry more weight on Microsoft Copilot.
Do AI models cite local-language content for hotels?
Outside English-speaking markets, models vary sharply in how often they cite local-country domains. Using country-code top-level domains as a proxy for local-language sourcing, the local-domain citation rate ranges from 38.5% (Swedish) down to 18.8% (Italian). English prompts are excluded, English content overwhelmingly lives on generic domains.
Even in the most localized market, more than three in five cited sources are non-local, a sign that English-language and global travel media dominate hotel discovery far beyond the English-speaking world. Swedish is the clear outlier, likely because the .se ecosystem is well-developed and less substitutable by global .com travel media.
Which AI model relies most on local sources for hotels?
The localization gap between models is large and consistent. Microsoft Copilot and Google AI Overview cite local-domain sources far more often than Grok in nearly every language tested.
Microsoft Copilot relies most on local sources overall (35.4% of its citations in non-English prompts), closely matched by Google AI Overview (35.5%), while Grok defaults to English hardest (17.4%) despite being the highest-volume model. For a hotel competing in Sweden, Germany, or the Netherlands, local-domain presence pays off on Microsoft Copilot but does comparatively little for Grok visibility.
Should hotels optimize for each AI model separately?
Yes, almost entirely. When the four models answer the same hotel questions, their top-cited domains barely converge. We compared the 20 most-cited domains for each model and measured how much they share.
Grok and Google AI Overview are the most similar pair, sharing 37.9% of their top sources; Microsoft Copilot and Google AI Overview the least, at 17.6%. A hotel visible on one model can be absent from three-quarters of the sources another model cites.
How many sources does each AI model cite per answer?
The models disagree even on how many sources to cite per answer.
Grok's breadth is why it dominates raw citation counts (156,000+ cited sources), but those extra slots skew toward high-volume global travel media, so breadth does not mean a level field.
Context
This analysis draws from Temso's AI visibility monitoring platform, which tracks how brands appear in AI model responses across ChatGPT, Microsoft Copilot, Grok, and Google AI Overview. The dataset covers hotel-related prompts across English and six additional language markets (Spanish, French, German, Dutch, Swedish, Italian) over roughly sixteen weeks (March 10 to June 29, 2026).
It measures what AI models cite, not web traffic, booking conversions, or the underlying quality of any hotel. These findings reflect citation behavior for hotel-related prompts in the monitored markets, and may not generalize to all hotel brands, prompt types, or other industry verticals.
Methodology
How we measured this
We tracked four AI models responding to hotel prompts in local languages across the monitored markets. Each response was parsed to extract source citations: the URLs, domains, and metadata referenced. Domain categories (editorial, commercial, UGC, reference, institutional) were assigned from a global domain registry; the 4–10% of citations left unclassified per model are shown as a separate "Other" category rather than redistributed. Cross-model overlap was measured using Jaccard similarity on each model pair's top-20 most-cited domains, reported as the average across all six pairs (25.3%) with the full range (17.6%–37.9%). Temporal analysis split the observation period at its midpoint and compared domain rankings in each half. Model coverage varied across the observation window, so temporal movements are reported as relative ranks within each half.
The analysis covers over 260,000 cited sources from 27,808 AI responses. Sample sizes exceeded recommended thresholds for every reported finding, and sources-per-response means carry tight 95% confidence intervals (e.g., Grok 50.4 [50.1, 50.7]). Localization is measured by a country-code-domain proxy (the source records carry no detected content language), so those figures are a floor, directional rather than exact, and exclude English-language prompts, which sit overwhelmingly on generic domains. Google AI Overview only produced reportable localized volume in Spanish and Italian in this window.
Frequently asked questions
Do AI models cite different sources for hotel recommendations?
Yes, dramatically so. Any two of the four models share only about 25% of their top-20 cited hotel domains, so roughly three in four sources differ. Microsoft Copilot and Google AI Overview overlap the least, at 17.6%.
Which AI model cites the most sources per answer for hotels?
Grok, by a wide margin, about 50 cited sources per answer, versus roughly 11 for Google AI Overview, 7 for Microsoft Copilot, and 7 for ChatGPT.
What kinds of websites do AI models cite for hotels?
It varies by model. Microsoft Copilot is overwhelmingly commercial (55.9%), citing booking platforms and hotel-brand sites; ChatGPT is the most editorial (41.7%) and the only model to meaningfully cite reference sources like Wikipedia (5.6%); Google AI Overview also tilts commercial (43.4%).
Do hotels need local-language web content for AI visibility?
For some models. Microsoft Copilot and Google AI Overview cite local-country domains most (about 35% of their citations in non-English prompts), so local-domain presence helps there. Grok cites local domains far less (17.4%), favoring global English travel media. It also helps most in Sweden (38.5%) and least in Italy (18.8%).
Which AI model relies most on local sources for hotels?
Microsoft Copilot and Google AI Overview, roughly tied at about 35% of their citations in non-English prompts going to local-country domains, versus Grok's 17.4%. Grok stays on global, largely English travel media regardless of the prompt's language.
How fast do AI hotel source rankings change?
Quickly. Over the ~16-week window, brand-direct hotel sites (Fairmont, Four Seasons, Hilton, Marriott) climbed in relative rank while travel-media aggregators (Forbes Travel Guide, Design Hotels) and OTAs (Hotels.com, Expedia) fell. We report relative rank because second-half citation volume was lower overall for data-collection reasons.
Is one AI ranking enough to optimize for in the hotel industry?
No. The models share only about a quarter of their top sources and weight them very differently, so visibility on one model does not transfer to the others. TripAdvisor is the only domain that ranks highly across all four, and even its rank varies. An AI-visibility strategy for hotels has to be built model by model, and, outside English, language by language.
